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A Semi-Blind Reference Watermarking Scheme Using DWT-DCT

Jan 14, 2017




  • International Journal of Computer Science & Information Technology (IJCSIT) Vol 4, No 2, April 2012

    DOI : 10.5121/ijcsit.2012.4206 69

    A Semi-Blind Reference Watermarking Scheme Using DWT-DCT-SVD for Copyright Protection

    Satyanarayana Murty. P1, M.Uday Bhaskar2, P. Rajesh Kumar3

    1Sr.Associate Professor, Department of ECE, GIITS, Vishakapatnam, India [email protected]

    2 PG Student, Department of CSE,Sri Sai Aditya Institute Of Science & Technology, Andhra Pradesh, India

    [email protected]

    3 Associate Professor, Department of ECE, AU college of Engineering, Vishakapatnam, India


    In this paper we propose a semi-blind watermarking scheme using Discrete Wavelet Transform,

    Discrete Cosine Transform and Singular Value Decomposition for copyright protection. We used a gray

    scale image as a watermark to hide in another gray scale image as a cover image. The cover image is

    modified (Zig-Zag) and divided to number of blocks of size n x n. We find the spatial frequency of each

    block and kept a threshold on this spatial frequency to form a reference image. Then the reference image

    is transformed into wavelet domain. A DCT is applied to the HF band of DWT decomposition reference

    image. We hide the watermark into reference image by modifying the singular values of transformed

    DCT coefficients with the singular values of watermark. The proposed algorithm provides a good

    imperceptibility and robust for various attacks.


    Spatial frequency, DWT, DCT, SVD, Zig_Zag, Reference image.


    Due to the rapid and extensive growth of network technology, digital information can now be distributed much faster and easier. However, according to the insufficient cognizance of intellectual property, the condition of illegal copies and spread of copyright reserved information are growing serious. To protect the copyright of multimedia information and to decrease the impulse to copy and spread copy right reserved multimedia information. Fortunately, the digital watermarking technique is the right choice to protect the right full information of the owners. Watermarking has two processes. One is embedding; a mark or signal is hiding into multimedia content as cover information. Second one is extraction; the hidden mark or signal at receiving end.

    In digital image watermarking the inserted watermark should not degrade the visual

    perception of an original image. The first method for hiding watermarking is by directly changing original cover-media. The advantages are simple and fast calculated but cannot protect itself from varied signal processing attacking [1, 2, 3]. The most of watermarking techniques embed the information data in the coefficients of transformation domain of the cover image, such as Fourier transformation, discrete cosine transformation, wavelet transformation and singular value decomposition. Image watermarking algorithms using Discrete Cosine Transform (DCT) [4, 5, 6, 12, 13], Discrete Wavelet Transform (DWT) [7,8,9,10,11] Singular Value Decomposition (SVD) [14,15] are available in the literature.

  • International Journal of Computer Science & Information Technology (IJCSIT) Vol 4, No 2, April 2012


    Domain transformation watermarking schemes, in general, first use DCT and DWT

    and then transforms the image into the spatial domain. Watermarking schemes usually uses a watermarking on black and white or greyscale images. Hybrid domain transforms are also available in the literature DCT- SVD [16,17,18] and DWT-SVD [19,20,21,22,23,24,25,26,27]. In this paper we proposed an optimal watermarking technique based on DWT- DCT-SVD. The rest of the paper is organized as follows: Section 2 describes the related work, while Section 3 contains our proposed algorithm and in section 4 experimental results followed by conclusions in Section 5.

    The DCT transform:

    The Discrete Cosine Transform (DCT) is a technique that converts a spatial domain waveform into its constituent frequency components as represented by a set of coefficients. These transforms are the members of real-valued discrete sinusoidal unitary transforms. The DCT has excellent energy compaction for highly correlated data. A 2D DCT can be computed as two separate one-dimensional transforms. This is shown in equation 1. The DCT transformed coefficients have a Dc component and AC component. The AC components are used for watermarking embedding, which provides good robust against various attacks.

    , = , 2 + 12 2 + 12 1

    f or u, v =0, 1, 2,.N-1. Where

    =)(u 2


    u=0 1 u=1,2,.N-1

    =)(v 2


    v=0 1 v=1,2,.N-1

    The DWT transform:

    The Discrete Wavelet Transform (DWT) is obtained by filtering the signal through a series of digital filters at different scales. The scaling operation is done by changing the resolution of the signal by the process of sub sampling. The analysis filter bank consists of a low-pass and high-pass filter at each decomposition stage. When the signal passes through these filters, it splits into two bands. The low-pass filter, which corresponds to an averaging operation, extracts the coarse information of the signal. The high-pass filter, which corresponds to a differencing operation, extracts the detail information of the signal. The output of the filtering operation is decimated by two. A two dimensional transform is accomplished by performing two separate one-dimensional transforms. First, the image is filtered along the row and decimated by two. It

  • International Journal of Computer Science & Information Technology (IJCSIT) Vol 4, No 2, April 2012


    is then followed by filtering the sub-image along the column and decimated by two. This operation splits the image into four bands, namely, LL, LH, HL and HH respectively as shown figure 1.

    Figure -1

    Singular Value Decomposition:

    In linear algebra, the singular value decomposition (SVD) is an important factorization of a rectangular real or complex matrix, with several applications in signal processing and statistics. The SVD of rectangle matrix A is a decomposition of the form

    = !"#2 Where A is an m n matrix. U, V are orthogonal matrices. S is a diagonal matrix composed of singular values of A. The singular values S1 S2 S3 . . . . . . . . . . . Sn 0 appear in descending order along the main diagonal of S.

    Spatial Frequency

    Spatial frequency of an image can be used to know the overall activity level in an

    image [9]. For an image block $ of size M N, the spatial frequency is defined as: !% = &'%( + %(3

    Where RF and CF are the row and column frequencies and are defined as:

    '% =* 1+ ,$-, . $-, . 10(

    12 4

    % = * 1+ ,$-, . $- 1, .0(

    12 5












    2 LL BAND




    X [m, n]

    Row Processing Column processing Row Column Processing

  • International Journal of Computer Science & Information Technology (IJCSIT) Vol 4, No 2, April 2012



    S S Bedi proposed a new SVD based and DCT-DWT oriented digital watermarking scheme. The middle band DCT coefficients are chosen to achieve high robustness against JPEG compression. He used a LL band of DWT to insert a watermark.[ 28 ]. Navas K A proposed a DWT-DCT-SVD watermarking algorithm, which over comes the problems of traditional watermarking. [ 29 ]. In this paper, they proposed a robust watermarking technique which combines features of discrete wavelet transformation (DWT), discrete cosine transformation and singular value decomposition. In this technique DWT is used to decompose the colour images into various frequency and time scale. Block DCT is applied on DWT coefficients of various frequencies to provide high level of robustness. DCT transformed blocks of size 4x4 are further decomposed using dual SVD technique to get singular values in which watermark is to be hidden.[ 30 ].

    In this paper the authors proposed an algorithm which combines the advantages DWT-

    DCT-SVD. Also they used the Arnold transform before embedding the watermark into original image. This improves the robustness of algorithm. [31]. A novel watermarking algorithm for digital image based on DWT-DCT-SVD was proposed in this paper. They applied four layers DWT on the original image and choose the low-frequency sub image and three high frequency sub images of the fourth layer. Then, by using DCT, SVD and the decomposition criteria proposed in this paper, they embedded the low-frequency sub-image and the three high frequency sub images obtained watermarking image into those of the original image adaptively.[ 32]. In this paper, authors presented a hybrid watermarking scheme based on DWT-DCT-SVD. They used the individual advantages of DWT, DCT and SVD. So they got good imperceptibility and robustness against various attacks. [33].

    Images lake Lena Bridge Lvroom Boat Mandrill Pirate Peppers

    PSNR 48.35 47.42 48.40 48.98 44.41 44.97 45.06 44.20

    Table 3 PSNR values


    3.1 Watermark Embedding

    The Watermark Embedding Procedure as Shown in Figure 2(a)

  • International Journal of Computer Science & Information Technology (IJCSIT) Vol 4, No 2, April 2012


    Figure 2(a)